# 导入包
import pandas as pd
import statsmodels.formula.api as smf
from sklearn.model_selection import train_test_split
from sqlalchemy import create_engine
from sklearn.metrics import r2_score, mean_squared_error
from matplotlib import pyplot as plt
import statsmodels.api as sm
import numpy as np
import seaborn as sns
import pymysql

db_config = {
    'host': 'localhost',
    'user': 'root',
    'password': '123456',
    'database': 'tushare1',
    'port': 3306,
    'charset': 'utf8mb4'  # 添加字符集设置
}
engine = create_engine(
    f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?charset={db_config['charset']}"
)
conn = pymysql.connect(**db_config)
chunk_size = 10000

# 获取华夏银行日线数据
df = pd.read_sql_query(
        """
        SELECT d.*, m.buy_lg_vol, m.sell_lg_vol, m.buy_elg_vol, m.sell_elg_vol, m.net_mf_vol,  i.vol as i_vol, i.closes as i_closes
        FROM date_1 d
        join moneyflows m on d.ts_code = m.ts_code and d.trade_date = m.trade_date
        left join index_daily i on d.trade_date = i.trade_date and i.ts_code = '000001.SH'
        WHERE d.trade_date BETWEEN '2023-01-01' AND '2023-12-31' AND d.ts_code='002229.SZ'
        """, 
        conn, 
        chunksize=chunk_size
    )
df1 = pd.concat(df, ignore_index=True)

df1['zd_closes'] = round(((df1['closes'] - df1['closes'].shift(1)) / df1['closes'].shift(1)), 2)
df1['zs_closes'] = round(((df1['i_closes'] - df1['i_closes'].shift(1)) / df1['i_closes'].shift(1)), 2)
df1['zs_vol'] = round(((df1['i_vol'] - df1['i_vol'].shift(1)) / df1['i_vol'].shift(1)), 2)
print(df1.head)

# 处理缺失数据
df1 = df1.dropna(subset=['zd_closes', 'zs_closes', 'zs_vol'])
print(df1.head)

ex = [ 'id', 'ts_code', 'trade_date', 'the_date', 'opens', 'high', 'low', 'closes', 'pre_closes', 'changes', 'pct_chg']
number = df1.select_dtypes(include=['number']).columns.to_list()
newList = [col for col in number if col not in ex]

formuls = 'zd_closes ~ ' + ' + ' .join(newList)
res = smf.ols(formuls, data=df1).fit()
print(res.summary())

plt.figure(figsize=(10, 6))
plt.scatter(res.fittedvalues, df1['zd_closes'])
plt.show()
features = df1[['vol', 'amount', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol']]
correlation_matrix = features.corr()

plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', cbar=True, fmt='.2f', linewidths=.5)
plt.show()